• Journal of the Korean Society of Radiology
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• v.7 no.1
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• pp.37-44
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• 2013

In study suggested clinical availability to shoulder forced traction method in term of quality of image, the patient's convenience and stability, according to whether to use of shoulder forced traction bend using computed tomography(CT) that X-ray calibration and various mathematic calibration algorithm application can be applied by AEC. To achieve this, 79 patients is complaining of cervical pain oriented that shoulder forced traction bend use the before and after acquires lateral projection scout image and transverse image. transverse image of a fixed size in concern field of pixel and figure the average HU value compare that quantitative analysis. Artifact and pixel and resolution to qualitative clinical estimation image analysis. the patient feel inconvenience degree that self-diagnosis survey that estimate. As a result, lateral projection scout image if you used shoulder forced traction bend for the depicted has been an increase in the number of a cervical vertebrae. transverse image concern field shoulder forced traction bend use the before and after for pixel and the average HU-value changes was judged to be almost irrelevant. Artifact and resolution and contrast, in qualitative analysis of the results relating the observer to the unusual result. So, the patients of 82.27% complained discomfort that use of shoulder forced traction bend in self-diagnosis survey. No merit of medical image by using of bend from result was analyzed quality of image to quantitative and qualitative method judged. Nowadays, CT is supplied possible revision of quality of radiation by reduction of slice and automatic exposure controller, etc and application of preconditioning filter process due to various mathematic revision algorithm. So, image noise by beam hardening artifact should not be a problem. shoulder forced traction bend of use no longer judged clinically availability because have not influence of image quality and give discomfort, have extra dangerousness.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.4 s.316
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• pp.42-47
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• 2007

To reduce the chattering errors of reed switch sensors in the automatic remote measurement of water meter a reed switch sensor was analyzed and improved. The operation of reed switch sensors can be described as a mechanical contact switch by approximation of permanent magnet piece to generate an electrical pulse. The reed switch sensors are used mostly in measurement application to detect the rotational or translational displacement. To apply for water flow measurement devices, the reed switch sensors should keep high reliability. They are applied for the electronic digital type of water flow meters. The reed switch sensor is just mounted simply on the conventional mechanical type flow meter. A small magnet is attached on a pointer of the water meter counter rotor. Inside the reed sensor two steel leaf springs make mechanical contact and apart repeatedly as rotation of flow meter counter. The counting electrical contact pulses can be converted as the water flow amount. The MCU sends the digital flow rate data to the server using the wireless communication network. But the digital data is occurred difference or won by chattering noise. The reed switch sensor contains chattering error by it self at the force equivalent position. The vibrations such as passing vehicle near to the switch sensor installed location causes chattering. In order to reduce chattering error, most system uses just software methods, for example using filter algorithm and also statistical calibration methods. The chattering errors were reduced by changing leaf spring structure using mechanical characteristics.

• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.6
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• pp.915-936
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• 2017

In this study, current status of Korean hazard mitigation guideline for tunnel operation is summarized. It shows that requirement for CCTV installation has been gradually stricted and needs for tunnel incident detection system in conjunction with the CCTV in tunnels have been highly increased. Despite of this, it is noticed that mathematical algorithm based incident detection system, which are commonly applied in current tunnel operation, show very low detectable rates by less than 50%. The putative major reasons seem to be (1) very weak intensity of illumination (2) dust in tunnel (3) low installation height of CCTV to about 3.5 m, etc. Therefore, an attempt in this study is made to develop an deep-learning based tunnel incident detection system, which is relatively insensitive to very poor visibility conditions. Its theoretical background is given and validating investigation are undertaken focused on the moving vehicles and person out of vehicle in tunnel, which are the official major objects to be detected. Two scenarios are set up: (1) training and prediction in the same tunnel (2) training in a tunnel and prediction in the other tunnel. From the both cases, targeted object detection in prediction mode are achieved to detectable rate to higher than 80% in case of similar time period between training and prediction but it shows a bit low detectable rate to 40% when the prediction times are far from the training time without further training taking place. However, it is believed that the AI based system would be enhanced in its predictability automatically as further training are followed with accumulated CCTV BigData without any revision or calibration of the incident detection system.